Spatio-temporal Attention-based Hidden Physics-informed Neural Network for Remaining Useful Life Prediction
This work addresses prognostic health management for industrial systems, representing an incremental improvement by integrating physics constraints with attention mechanisms.
The paper tackles the problem of low accuracy and interpretability in predicting Remaining Useful Life (RUL) for industrial systems by introducing a Spatio-temporal Attention-based Hidden Physics-informed Neural Network (STA-HPINN), which achieves exceptional performance on a benchmark dataset compared to cutting-edge methods.
Predicting the Remaining Useful Life (RUL) is essential in Prognostic Health Management (PHM) for industrial systems. Although deep learning approaches have achieved considerable success in predicting RUL, challenges such as low prediction accuracy and interpretability pose significant challenges, hindering their practical implementation. In this work, we introduce a Spatio-temporal Attention-based Hidden Physics-informed Neural Network (STA-HPINN) for RUL prediction, which can utilize the associated physics of the system degradation. The spatio-temporal attention mechanism can extract important features from the input data. With the self-attention mechanism on both the sensor dimension and time step dimension, the proposed model can effectively extract degradation information. The hidden physics-informed neural network is utilized to capture the physics mechanisms that govern the evolution of RUL. With the constraint of physics, the model can achieve higher accuracy and reasonable predictions. The approach is validated on a benchmark dataset, demonstrating exceptional performance when compared to cutting-edge methods, especially in the case of complex conditions.